A framework for federal health data collection

1976 ◽  
Author(s):  
N. Phillip Ross ◽  
Meyer Katzper
Keyword(s):  
Data Collection ◽  
Health Data

scholarly journals Opportunities and Challenges in Public Health Data Collection in Southern Asia: Examples from Western India and Kathmandu Valley, Nepal

2017 ◽  
Vol 9 (7) ◽  
pp. 1106 ◽  
Author(s):  
Amruta Nori-Sarma ◽  
Anobha Gurung ◽  
Gulrez Azhar ◽  
Ajit Rajiva ◽  
Dileep Mavalankar ◽  
...  
Keyword(s):  
Public Health ◽  
Data Collection ◽  
Health Data ◽  
Kathmandu Valley ◽  
Western India ◽  
Southern Asia ◽  
Public Health Data

Understanding Data Collection Mechanisms Used by Health and Wellness Applications (Preprint)

2021 ◽  
Author(s):  
Ben Philip ◽  
Mohamed Abdelrazek ◽  
Alessio Bonti ◽  
Scott Barnett ◽  
John Grundy
Keyword(s):  
Data Collection ◽  
User Experience ◽  
Health Data ◽  
Domain Model ◽  
Health And Wellness ◽  
Related Services ◽  
Related Data ◽  
Medical Apps ◽  
App Development ◽  
Health Related

UNSTRUCTURED Our objective is to better understand health-related data collection across different mHealth app categories. This would help in developing a health domain model for mHealth apps to facilitate app development and data sharing between these apps to improve user experience and reduce redundancy in data collection. We identified app categories listed in a curated library which was then used to explore the Google Play Store for health/medical apps that were then filtered using our inclusion criteria. We downloaded and analysed these apps using a script we developed around the popular AndroGuard tool. We analysed the use of Bluetooth peripherals and built-in sensors to understand how a given app collects/generates health data. We retrieved 3,251 applications meeting our criteria, and our analysis showed that only 10.7% of these apps requested permission for Bluetooth access. We found 50.9% of the Bluetooth Service UUIDs to be known in these apps, with the remainder being vendor specific. The most common health-related services using the known UUIDs were Heart Rate, Glucose and Body Composition. App permissions show the most used device module/sensor to be the camera (20.57%), closely followed by GPS (18.39%). Our findings are consistent with previous studies in that not many health apps were found to use built-in sensors or peripherals for collecting health data. The use of more peripherals and automated data collection along with integration with other apps could increase usability and convenience which would eventually also improve user experience and data reliability.

scholarly journals Engagement and Participant Experiences With Consumer Smartwatches for Health Research: Longitudinal, Observational Feasibility Study (Preprint)

2019 ◽  
Author(s):  
Anna L Beukenhorst ◽  
Kelly Howells ◽  
Louise Cook ◽  
John McBeth ◽  
Terence W O'Neill ◽  
...  
Keyword(s):  
Data Collection ◽  
Knee Osteoarthritis ◽  
Patient Reported Outcomes ◽  
Health Data ◽  
Self Report ◽  
Sensor Data ◽  
Battery Life ◽  
Activity Data ◽  
Technical Problems ◽  
Patient Reported

BACKGROUND Wearables provide opportunities for frequent health data collection and symptom monitoring. The feasibility of using consumer cellular smartwatches to provide information both on symptoms and contemporary sensor data has not yet been investigated. OBJECTIVE This study aimed to investigate the feasibility and acceptability of using cellular smartwatches to capture multiple patient-reported outcomes per day alongside continuous physical activity data over a 3-month period in people living with knee osteoarthritis (OA). METHODS For the KOALAP (Knee OsteoArthritis: Linking Activity and Pain) study, a novel cellular smartwatch app for health data collection was developed. Participants (age ≥50 years; self-diagnosed knee OA) received a smartwatch (Huawei Watch 2) with the KOALAP app. When worn, the watch collected sensor data and prompted participants to self-report outcomes multiple times per day. Participants were invited for a baseline and follow-up interview to discuss their motivations and experiences. Engagement with the watch was measured using daily watch wear time and the percentage completion of watch questions. Interview transcripts were analyzed using grounded thematic analysis. RESULTS A total of 26 people participated in the study. Good use and engagement were observed over 3 months: most participants wore the watch on 75% (68/90) of days or more, for a median of 11 hours. The number of active participants declined over the study duration, especially in the final week. Among participants who remained active, neither watch time nor question completion percentage declined over time. Participants were mainly motivated to learn about their symptoms and enjoyed the self-tracking aspects of the watch. Barriers to full engagement were battery life limitations, technical problems, and unfulfilled expectations of the watch. Participants reported that they would have liked to report symptoms more than 4 or 5 times per day. CONCLUSIONS This study shows that capture of patient-reported outcomes multiple times per day with linked sensor data from a smartwatch is feasible over at least a 3-month period. INTERNATIONAL REGISTERED REPORT RR2-10.2196/10238

Advances in Monitoring Have Not Translated into Improvements in Humanitarian Health Services

2007 ◽  
Vol 22 (5) ◽  
pp. 384-389 ◽  
Author(s):  
Les Roberts
Keyword(s):  
Data Collection ◽  
Local Level ◽  
Health Data ◽  
Health Priorities ◽  
Political Issues ◽  
Life Saving ◽  
The Us ◽  
Control And Prevention ◽  
Political Actions

AbstractBackground:This paper is an attempt to review the advances and shortfalls in data collection and use of health data that have occurred during health emergencies in recent decades for the opening session of the Humanitarian and Health Conference at Dartmouth University in September of 2006.Methods:Examples of various kinds of successes and failures associated with health data collection are given to highlight advances with an effort to emphasize multi-agency efforts reviewed by outside scholars.Results:Health data, particularly surveillance data, have allowed relief workers to set priorities for life-saving humanitarian programs. The main guidelines widely utilized such as those of the US Centers for Disease Control and Prevention, Médecins sans Frontières, and the Sphere Project have considerable similarity due to the consistency of data collected in various crises. Moreover, difficult to see problems and successes have been revealed by coherent surveillance efforts. Yet, these data collection efforts can not show significant improvements in the quality of humanitarian aid in recent years. Moreover, health data often do not appear to have meaningful influence on the prioritizing of relief resources globally or on those political issues that trigger emergencies.Conclusions:The field of humanitarian relief is relatively nascent. Methods for documenting basic health measures on the local level have been developed and general health priorities have been documented. Some technical improvements in monitoring still are needed but decision-making is most often limited by the lack of data rather than the problems with data. The ability of health data to influence spending global priorities, legal or political actions undertaken by international organizations, remains very limited.

scholarly journals Data Classification for Secure Mobile Health Data Collection Systems

2020 ◽  
Vol 5 ◽  
pp. 100054
Author(s):  
Marriette Katarahweire ◽  
Engineer Bainomugisha ◽  
Khalid A. Mughal
Keyword(s):  
Data Collection ◽  
Mobile Health ◽  
Data Classification ◽  
Health Data

Issues and Challenges in Securing eHealth Systems

2014 ◽  
Vol 5 (1) ◽  
pp. 1-19 ◽  
Author(s):  
Yan Bai ◽  
Lirong Dai ◽  
Juan Li
Keyword(s):  
Data Collection ◽  
Recent Literature ◽  
Health Data ◽  
Future Research ◽  
Research Directions ◽  
Future Research Directions ◽  
Ehealth Services

With the widespread eHealth usage, security of eHealth services is becoming increasingly important. In this paper, we analyze the security problems in eHealth systems, discuss various approaches of securing health data collection and sharing proposed in the recent literature on eHealth security, and provide comparative evaluations that include advantages and limitations of each approach. Possible future research directions on each approach for enhancing security for eHealth applications are also suggested.

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